Suchergebnisse - Machine Vision and Applications

Fujii, K. “… This open access book provides cutting-edge work on machine learning in sports analytics, emphasizing the integration of computer vision, data analytics, and machine learning to redefine strategic sports analysis.  …”

Dhahbi, W., Jebabli, N., Boujabli, M., Souaifi, M., Dergaa, I., Ezzdine, L. B. “… This review examined data collection modes, analytical approaches, and practical implementation in laboratory and field settings. Results: AI applications evolved from basic statistical modeling to sophisticated machine learning configurations, demonstrating superior performance in technique analysis (94% agreement with international judges), individualized training prescription (25% improvement over baseline), and injury risk forecasting (85% pre-competition accuracy).  …”

Edriss, S., Romagnoli, C., Caprioli, L., Bonaiuto, V., Padua, E., Annino, G. “… The findings suggest that 2D systems offer economic and straightforward solutions, but they still face limitations in capturing out-of-plane movements and environmental factors. Merging vision sensors with built-in artificial intelligence and machine learning software to create 2D-to-3D pose estimation is highlighted as a promising method to address these challenges, supporting the broader adoption of markerless motion analysis in future kinematic and biomechanical research. …”

Lubysheva L. I. “… Based on the results of content analysis, the main trends in the development of digital transformation of physical culture and sports are identified: digitalization and virtualization in education, digitalization and virtualization in sports, gamification, artificial intelligence and machine learning. The current state of manifestation of the considered trends causes the formation of digital transformation trends, among which the key ones are: the use of wearable digital devices for monitoring the physical state of a person in education and sports, the use of mobile applications in training and managing the training process, the use of machine vision technologies in analyzing and modeling athlete movements, the development of. …”

Teixeira, J. E., Maio, E., Afonso, P., Encarnação, S., Machado, G. F., Morgans, R., Barbosa, T. M., Monteiro, A. M., Forte, P., Ferraz, R., Branquinho, L. “… Furthermore, collective dynamics and patterns were mapped by graph metrics such as betweenness centrality, eccentricity, efficiency, vulnerability, clustering coefficient, and page rank, expected possession value, pitch control map classifier, computer vision techniques, expected goals, 3D ball trajectories, dangerousity assessment, pass probability model, and total passes attempted.  …”

Zhang, J., Guo, R., Zhu, Y., Che, Y., Zeng, Y., Yu, L., Yang, Z., Yang, J. “… In recent years, advances in artificial intelligence, machine vision, and the Internet of Things have significantly impacted sports analytics, particularly basketball, where accurate measurement and analysis of player performance have become increasingly important.  …”

Liu, Y., Hafemann, L. G.

Steinberg, C., Lleshi, R., Miko, H.

Beltrán Beltrán, R., Richter, J., Köstermeyer, G., Heinkel, U. “… To capture joint movements, we use a fourth-generation iPad Pro with LiDAR to record climbing sequences in which we convert the climber`s 2-D skeleton provided by the Vision framework from Apple into 3-D joints using the LiDAR depth information.  …”

Sharifi-Renani, M., Mahoor, M. H., Clary, C. W. “… Through wearable sensors and deep learning techniques, biomechanical analysis can reach beyond the lab for clinical and sporting applications. Transformers, a class of recent deep learning models, have become widely used in state-of-the-art artificial intelligence research due to their superior performance in various natural language processing and computer vision tasks.  …”

Skublewska-Paszkowska, M., Powroznik, P. “… Human Action Recognition is a challenging task used in many applications. It interacts with many aspects of Computer Vision, Machine Learning, Deep Learning and Image Processing in order to understand human behaviours as well as identify them.  …”

Mundt, M., Born, Z., Goldacre, M., Alderson, J. “… The findings of this study highlight the need for further evaluation of computer vision-based pose estimation models for application in biomechanical human modelling, and the limitations of machine learning-based GRF estimation models that rely on 2D keypoints.  …”

Moura, H. D., Kholkine, L., Van Damme, L., Mets, K., Leysen, C., De Schepper, T., Hellinckx, P., Latré, S.

Mundt, M., Oberlack, H., Goldacre, M., Powles, J., Funken, J., Morris, C., Potthast, W., Alderson, J. “… To increase the utility of legacy, gold-standard, three-dimensional (3D) motion capture datasets for computer vision-based machine learning applications, this study proposed and validated a method to synthesise two-dimensional (2D) video image frames from historic 3D motion data.  …”

Achermann, B., Oberhofer, K., Gross, M., Lorenzetti, S. “… It might be possible to correct these errors in future work using machine learning techniques. This pilot study shows the feasibility of a computer vision-based Python application to measure barbell kinematics in a low-cost manner and might play a part towards advancing VBT monitoring technologies for widespread use …”

Smith, P. D.

Voeikov, R., Falaleev, N., Baikulov, R.

Brumann, C., Kukuk, M., Reinsberger, C. “… At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences.  …”

Wu, Y., Wang, Y., Zhang, S., Ogai, H. “… As the foundation of intelligent systems, machine vision perceives the surrounding environment and provides a basis for decision-making.  …”

Santiago, C. B., Sousa, A., Reis, L. P. “… Machine Vision and Applications …”